Snowflake Study: AI Is Creating More Jobs Than It Cuts as Enterprise Adoption Accelerates

Artificial intelligence may be reshaping the workforce—but not necessarily shrinking it. According to new global research from Snowflake and Omdia by Informa TechTarget, most organizations deploying AI are actually adding jobs rather than eliminating them.

The report, “The ROI of Gen AI and Agents,” surveyed 2,050 business and technology leaders across 10 countries and found that 77% of organizations report increased hiring linked to AI adoption, compared with 46% that report role reductions.

The takeaway: while AI is certainly changing how work gets done, it’s more often reshaping roles than replacing them outright.

AI’s Workforce Impact Is Net Positive

The study suggests that AI’s influence on employment is far more nuanced than common narratives about automation-driven job losses.

Among respondents:

• 42% say AI has created jobs

• 11% say AI has eliminated roles

• 35% say it has both created and reduced jobs

That mix results in a net trend toward job growth. In fact, 69% of organizations experiencing both gains and losses say the overall workforce impact has been positive.

The effect also appears to strengthen as AI adoption matures. Organizations running multiple AI use cases report a 75% positive workforce impact, compared with 56% among companies still in early experimentation phases.

According to Anahita Tafvizi, Chief Data Analytics Officer at Snowflake, the difference often comes down to how organizations operationalize AI.

“The strongest ROI isn’t coming from experimentation alone,” Tafvizi said. “It’s coming from embedding AI into core operations while strengthening data readiness and governance.”

Technical Roles Are Seeing the Biggest Gains

Not all functions are affected equally. The report shows technical teams benefiting the most from AI-driven job growth, particularly in areas where AI amplifies productivity rather than fully automates tasks.

The strongest gains were reported in:

• IT operations: 56% reporting job increases

• Cybersecurity: 46% reporting gains

• Software development: 38% reporting gains

Interestingly, those same functions are also experiencing some of the largest job reductions, suggesting that AI is restructuring teams rather than simply expanding them.

For example, IT operations reported both the highest job gains and the highest losses, with 40% reporting role reductions. Similar trends appeared in customer support (37%) and data analytics (37%).

This dual effect points to a broader pattern: AI is automating certain tasks while simultaneously creating demand for new skills and capabilities.

Data Readiness Emerges as AI’s Biggest Bottleneck

Despite strong ROI signals, most organizations say scaling AI remains difficult—and the biggest obstacle isn’t the technology itself.

According to the report, 96% of organizations face challenges when attempting to scale AI initiatives.

The biggest barriers all revolve around data:

• 65% struggle to break down data silos

• 62% struggle to measure and monitor data quality

• 62% struggle to prepare data for AI use

Perhaps most striking, only 7% of organizations say more than half of their unstructured data is AI-ready.

Regional differences also emerged. India leads globally, with 14% reporting the majority of their unstructured data is AI-ready, followed by Australia and New Zealand (12%) and Canada (10%). The United States sits at 8%, close to the global average.

Shadow AI and Governance Risks Are Rising

The research also highlights growing governance challenges as AI tools spread across organizations.

More than half of employees (57%) report using non-approved AI tools, including 66% of C-suite executives.

At the same time, 60% of organizations say they need greater investment in data infrastructure and monitoring tools, while many leaders acknowledge governance enforcement remains difficult.

Among respondents:

• 22% of middle managers and individual contributors say enforcing data governance is “very challenging”

• 19% of executives say the same

These findings suggest that while AI adoption is accelerating, many organizations are still catching up with the governance frameworks required to manage it responsibly.

AI Is Delivering Real ROI

Despite the operational challenges, companies report strong financial returns from AI investments.

According to the research:

• Organizations report $1.49 in value for every $1 invested in AI

• 92% of early adopters report positive ROI

• Companies expect to allocate 22% of technology budgets to AI next year

These numbers indicate that AI is rapidly shifting from experimentation to core infrastructure.

Where AI Is Already Embedded

The report shows that AI adoption is most mature within technical and operational functions, where automation and data analysis deliver immediate efficiency gains.

Active AI usage rates include:

• IT operations: 62%

• Data analytics: 59%

• Cybersecurity: 53%

• Software development: 50%

Meanwhile, procurement, sales, and marketing remain slower adopters, with roughly 30% reporting active use.

Industry adoption patterns also vary. Advertising and media leads, with 42% reporting AI in production, followed by healthcare and life sciences (34%) and manufacturing and technology (32%).

AI Is Already Changing How Software Gets Built

One of the report’s most striking findings involves software development.

Respondents estimate that approximately 48% of code is now AI-generated, highlighting how deeply AI tools are embedded in developer workflows.

Organizations report strong benefits from these coding tools:

• 82% report improvements in testing, bug detection, and resolution

• 80% report better overall code quality

Snowflake is investing heavily in this area, including Cortex Code, its AI coding agent designed to operate on enterprise data environments.

The Future of Work Will Be Data-Driven

According to Adam DeMattia, Senior Director of Research at Omdia, the results suggest the next phase of AI adoption will hinge on data strategy rather than new algorithms.

“Organizations that can unify their data, improve quality, and operationalize AI responsibly will be best positioned to sustain ROI and workforce gains,” DeMattia said.

In other words, the AI race may ultimately be won not by companies with the most models—but by those with the best data foundations.

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